TY  - CONF
A1  - Fischer, Markus
A1  - Pourbafrani, Mahsa
A1  - Kemmerling, Marco
A1  - Stich, Volker
A2  - Nyhuis, Peter
A2  - Herberger, David
A2  - Hübner, Marco
T1  - A Framework for Online Detection and Reaction to Disturbances on the Shop Floor Using Process Mining and Machine Learning
T2  - Proceedings of the 1st Conference on Production Systems and Logistics (CPSL 2020)
N2  - The shop floor is a dynamic environment, where deviations to the production plan frequently occur. While there are many tools to support production planning, production control is left unsupported in handling disruptions. The production controller evaluates the deviations and selects the most suitable countermeasures based on his experience. The transparency should be increased in order to improve the decision quality of the production controller by providing meaningful information during his decision process. In this paper, we propose a framework in which an interactive production control system supports the controller in the identification of and reaction to disturbances on the shop floor. At the same time, the system is being improved and updated by the domain knowledge of the controller. The reference architecture consists of three main parts. The first part is the process mining platform, the second part is the machine learning subsystem that consists of a part for the classification of the disturbances and one part for recommending countermeasures to identified disturbances. The third part is the interactive user interface. Integrating the user’s feedback will enable an adaptation to the constantly changing constraints of production control. As an outlook for a technical realization, the design of the user interface and the way of interaction is presented. For the evaluation of our framework, we will use simulated event data of a sample production line. The implementation and test should result in higher production performance by reducing the downtime of the production and increase in its productivity.
KW  - decision support
KW  - production control
KW  - process mining
KW  - machine learning
KW  - Internet of Production
KW  - disturbance management
KW  - deviation detection
KW  - rev
Y1  - 2020
UR  - https://epub.fir.de/frontdoor/index/index/docId/316
SP  - 387
EP  - 396
PB  - Institute for Production and Logistics Research GbR
CY  - Hannover
ER  -